Abstract: Devices, methods, and non-transitory program storage devices (NPSDs) for an improved, so-called “hybrid” image registration process are disclosed herein, comprising: obtaining a first set of captured images, wherein the first set of captured images comprises a reference image and one or more bracketed images; and for each of the one or more bracketed images: performing a first (e.g., global) registration operation and a second (e.g., dense, or other localized) registration operation on the bracketed image with respect to the reference image, wherein each of the first and second registration operations produces an output; generating a blend map for the bracketed image, wherein each value in the blend map indicates whether to use the first or second registration operation output for a corresponding one or more pixels when registering the bracketed image with the reference image; and registering the bracketed image with the reference image, according to the generated blend map.

Abstract: In one implementation, a method includes obtaining an image. The method includes splitting the image to produce a high-frequency component image and a low-frequency component image. The method includes downsampling the low-frequency component image to generate a downsampled low-frequency component image. The method includes correcting color aberration of the downsampled low-frequency component image to generate a color-corrected downsampled low-frequency component image. The method includes upsampling the color-corrected downsampled low-frequency component image to generate a color-corrected low-frequency component image. The method includes combining the color-corrected low-frequency component image and the high-frequency component image to generate a color-corrected version of the image.

Abstract: A light source module includes an array of illumination elements and an optional projecting lens. The light source module is configured to receive or generate a control signal for adjusting different ones of the illumination elements to control a light field emitted from the light source module. In some embodiments, the light source module is also configured to adjust the projecting lens responsive to objects in an illuminated scene and a field of view of an imaging device. A controller for a light source module may determine a light field pattern based on various parameters including a field of view of an imaging device, an illumination sensitivity model of the imaging device, depth, ambient illumination and reflectivity of objects, configured illumination priorities including ambient preservation, background illumination and direct/indirect lighting balance, and so forth.

Abstract: In one embodiment, a method includes obtaining an image comprising a plurality of pixels, determining, for a particular pixel of the plurality of pixels, a gradient value, classifying, based on the gradient value, the particular pixel into a flat class or one of a plurality of edge classes, and denoising the particular pixel based on the classification.

Abstract: Devices, methods, and computer-readable media are disclosed, describing an adaptive, subject-aware approach for image bracket selection and fusion, e.g., to generate high quality images in a wide variety of capturing conditions, including low light conditions. An incoming image stream may be obtained from an image capture device, comprising images captured using differing default exposure values, e.g., according to a predetermined pattern. When a capture request is received, it may be detected whether one or more human or animal subjects are present in the incoming image stream. If a subject is detected, an exposure time of one or more images selected from the incoming image stream may be reduced relative to its default exposure time. Prior to the fusion operation, one of the selected images may be designated a reference image for the fusion operation based, at least in part, on a sharpness score and/or a blink score of the image.

Abstract: In one implementation, a method includes obtaining an image. The method includes splitting the image to produce a high-frequency component image and a low-frequency component image. The method includes downsampling the low-frequency component image to generate a downsampled low-frequency component image. The method includes correcting color aberration of the downs ampled low-frequency component image to generate a color-corrected downsampled low-frequency component image. The method includes upsampling the color-corrected downsampled low-frequency component image to generate a color-corrected low-frequency component image. The method includes combining the color-corrected low-frequency component image and the high-frequency component image to generate a color-corrected version of the image.

Abstract: In one implementation, a method includes obtaining an image. The method includes splitting the image to produce a high-frequency component image and a low-frequency component image. The method includes downsampling the low-frequency component image to generate a downsampled low-frequency component image. The method includes correcting color aberration of the downsampled low-frequency component image to generate a color-corrected downsampled low-frequency component image. The method includes upsampling the color-corrected downsampled low-frequency component image to generate a color-corrected low-frequency component image. The method includes combining the color-corrected low-frequency component image and the high-frequency component image to generate a color-corrected version of the image.

Abstract: In one implementation, a method includes obtaining an image. The method includes correcting color aberration of the image comprising, for a particular pixel of the image by determining one or more chromatic characteristic values for the particular pixel, determining a likelihood that the particular pixel exhibits color aberration based on the one or more chromatic characteristic values for the particular pixel, generating a filtered version of the particular pixel by filtering the particular pixel using pixels in a neighborhood surrounding the particular pixel, and generating a color-corrected version of the particular pixel based on the likelihood that the particular pixel exhibits color aberration and the filtered version of the particular pixel.

Abstract: In one embodiment, a method includes obtaining an image comprising a plurality of pixels. The method includes determining, for a particular pixel of the plurality of pixels, a feature value. The method includes selecting, based on the feature value, a set of selected pixels from a set of candidate pixels in an image region surrounding the particular pixel. The method includes denoising the particular pixel based on the set of selected pixels.

Abstract: Devices, methods, and computer-readable media describing an adaptive approach for image selection, fusion, and noise reduction, e.g., to generate low noise and high dynamic range (HDR) images with improved motion freezing in a variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images. When a capture request is received, a set of rules may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. The fusion operation may be designed to adaptively fuse the selected images, e.g., in a fashion that is determined to be optimal from a noise variance minimization standpoint. A fusion-adaptive noise reduction process may further be performed on the resultant fused image.

Abstract: An adaptive approach to image bracket determination and a more memory-efficient approach to image fusion, which are designed to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions, are described. An incoming preview image stream may be obtained from an image capture device. When a capture request is received, an analysis may be performed on an image from the preview image stream that has a predetermined temporal relationship to the image capture request. Based on the analysis, a set of images (and their respective capture parameters, e.g., exposure time) may be determined for the image capture device to capture. As the determined set of images are captured, they may be registered and fused in a memory-efficient manner that, e.g., places an upper limit on the overall memory footprint of the registration and fusion operations—regardless of how many images are captured in total.

Abstract: An incoming image stream may be obtained from an image capture device operating in low-light conditions and/or a simulated long exposure image capture mode. As images are obtained, a weighting operation may be performed on the pixels of the captured images to generate and/or update an accumulative weight map, wherein the weighting is based, e.g., on the proximity of the captured pixels' values to the respective image capture device's maximum observable pixel value. As batches of images are obtained, they may be fused, e.g., according to the accumulative weight map, in a memory-efficient manner that places an upper limit on the overall memory footprint of the fusion operations, to simulate an actual long exposure image capture. In some embodiments, the weight map may be stored at a lower resolution than the obtained images and then upscaled, e.g., via the use of guided filters, before being applied in the fusion operations.

Abstract: Devices, methods, and computer-readable media describing an adaptive approach for image selection, fusion, and noise reduction, e.g., to generate low noise and high dynamic range (HDR) images with improved motion freezing in a variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images. When a capture request is received, a set of rules may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. The fusion operation may be designed to adaptively fuse the selected images, e.g., in a fashion that is determined to be optimal from a noise variance minimization standpoint. A fusion-adaptive noise reduction process may further be performed on the resultant fused image.

Abstract: Embodiments relate to a bilateral filter circuit for directional filtering of an image. The directional bilateral filter circuit determines an edge direction and a weight for the edge direction by processing differences between pixel values of pixels in a first block of pixels in the image. The bilateral filter circuit determines non-directional taps for pixels in a second block by processing pixel locations, and determines directional taps by processing differences between pixel values, gradient information for the second block and the edge direction. The bilateral filter circuit determines final filter taps for pixels in the second block by blending corresponding non-directional taps and directional taps using the weight. The bilateral filter circuit obtains a pixel value of a filtered image by multiplying the final filter taps to corresponding pixel values of the pixels in the second block and adding the multiplied values.

Abstract: Embodiments relate to a bilateral filter circuit for directional filtering of an image. The directional bilateral filter circuit determines an edge direction and a weight for the edge direction by processing differences between pixel values of pixels in a first block of pixels in the image. The bilateral filter circuit determines non-directional taps for pixels in a second block by processing pixel locations, and determines directional taps by processing differences between pixel values, gradient information for the second block and the edge direction. The bilateral filter circuit determines final filter taps for pixels in the second block by blending corresponding non-directional taps and directional taps using the weight. The bilateral filter circuit obtains a pixel value of a filtered image by multiplying the final filter taps to corresponding pixel values of the pixels in the second block and adding the multiplied values.

Abstract: Embodiments relate to a bilateral filter circuit for directional filtering of an image. The directional bilateral filter circuit determines an edge direction and a weight for the edge direction by processing differences between pixel values of pixels in a first block of pixels in the image. The bilateral filter circuit determines non-directional taps for pixels in a second block by processing pixel locations, and determines directional taps by processing differences between pixel values, gradient information for the second block and the edge direction. The bilateral filter circuit determines final filter taps for pixels in the second block by blending corresponding non-directional taps and directional taps using the weight. The bilateral filter circuit obtains a pixel value of a filtered image by multiplying the final filter taps to corresponding pixel values of the pixels in the second block and adding the multiplied values.

Abstract: Embodiments relate to a bilateral filter circuit for directional filtering of an image. The directional bilateral filter circuit determines an edge direction and a weight for the edge direction by processing differences between pixel values of pixels in a first block of pixels in the image. The bilateral filter circuit determines non-directional taps for pixels in a second block by processing pixel locations, and determines directional taps by processing differences between pixel values, gradient information for the second block and the edge direction. The bilateral filter circuit determines final filter taps for pixels in the second block by blending corresponding non-directional taps and directional taps using the weight. The bilateral filter circuit obtains a pixel value of a filtered image by multiplying the final filter taps to corresponding pixel values of the pixels in the second block and adding the multiplied values.

Abstract: In various implementations a method includes obtaining a plurality of source images, stabilizing the plurality of source images to generate a plurality of stabilized images, and averaging the plurality of stabilized image to generate a synthetic long exposure image. In various implementations, stabilizing the plurality of source images includes: selecting one of the plurality of source images to serve as a reference frame; and registering others of the plurality of source images to the reference frame by applying a perspective transformation to others of the plurality of the source images.

Abstract: Techniques of reducing or eliminating artifact pixels in high dynamic range (HDR) imaging are described. One embodiment includes obtaining a first image of a scene at a first time with first exposure settings and obtaining a second image of the scene at a second time with second exposure settings that differ from the first exposure settings. The obtained images may be downsampled. The images may be compared to each other to assist with determining a number of potential artifact pixels in the scene. Depending on a relationship between the number of potential artifact pixels and a threshold value, the first image or second image may be selected as a reference image for registering the images with each other. A type of registration performed between the images may depend on which of the two images is the selected reference image. The registered images may be used to generate an HDR image.

Abstract: In various implementations, a method includes obtaining a target image having a first resolution and comprising a plurality of target pixels having a plurality of corresponding target pixel values, obtaining a guide image having a second resolution and comprising a plurality of guide pixels having a plurality of corresponding guide pixel values, and generating an enhanced target image based on the target image and the guide image, the enhanced target image having the second resolution and comprising a plurality of enhanced target pixels having a plurality of corresponding enhanced target pixel values. In various implementations, determining, for a particular upscaled target pixel, a similarity metric indicative of the similarity of a neighborhood of pixels around the particular upscaled target pixel to a neighborhood of pixels around a corresponding guide pixel, and determining, for the particular upscaled target pixel, an enhanced target pixel value based on the similarity metric.